2016 Colorado Financial Contributions
Jennifer (Jenni) D Sanders
========================================================
Basic Data
Univariate Plots Section
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.#that's a lot of receipts with a 0 amount. exclude the top 1% of contributions to zoom in a bit.
ggplotly(
ggplot(data = pfc, aes(x = contb_receipt_amt)) +
geom_histogram() +
#limit x axis to omit top 1% of val
xlim(0, quantile((pfc$contb_receipt_amt), 0.99))
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3032 rows containing non-finite values (stat_bin).#are there zip codes more likely to contribute?
plot_ly(x = pfc$contbr_zip, data = pfc, type = "histogram")#too specific. are there cities more likely to contribute?
plot_ly(x = pfc$contbr_city, data = pfc, type = "histogram")#I see you, Denver. Then Boulder, where people can work in Denver.#Are there occupations that are more likely to contribute?
plot_ly(x = pfc$contbr_occupation, data = pfc, type = "histogram")## Warning: Ignoring 32 observations#The largest groups are retirees. I did not expect retirees to be donating so actively.#Which candidate received more donations? I've heard Colorado is fairly purple.
plot_ly(x = pfc$cand_nm, data = pfc, type = "histogram")#Let's check that by party
plot_ly(x = pfc$cand_party, data = pfc, type = "histogram")Univariate Analysis
## cmte_id cand_id cand_nm
## Length:164206 P00003392:70843 Clinton, Hillary Rodham :70843
## Class :character P60007168:53689 Sanders, Bernard :53689
## Mode :character P80001571:13718 Trump, Donald J. :13718
## P60006111:12953 Cruz, Rafael Edward 'Ted':12953
## P60005915: 7112 Carson, Benjamin S. : 7112
## P60006723: 2201 Rubio, Marco : 2201
## (Other) : 3690 (Other) : 3690
## contbr_nm contbr_city contbr_st
## LENELL, MATT : 390 DENVER :32215 CO:164206
## IMMASCHE, SONIA : 356 BOULDER :14936
## SMITH, PHILIP : 320 COLORADO SPRINGS:11039
## CASPERSON, CAROLINA: 304 FORT COLLINS : 7494
## RAMSEY, WILLIAM : 261 AURORA : 6109
## HOFFMAN, TONI : 240 LITTLETON : 5461
## (Other) :162335 (Other) :86952
## contbr_zip contbr_employer contbr_occupation
## 804212057: 390 N/A :23483 RETIRED :38933
## 805241517: 356 RETIRED :20878 NOT EMPLOYED :16446
## 802212506: 322 SELF-EMPLOYED:12233 INFORMATION REQUESTED: 3861
## 802034502: 304 NONE :12047 ATTORNEY : 3229
## 814015616: 300 NOT EMPLOYED : 6761 TEACHER : 2536
## 80504 : 245 (Other) :88626 (Other) :99169
## (Other) :162289 NA's : 178 NA's : 32
## contb_receipt_amt contb_receipt_dt
## Min. :-16300.0 Min. :2014-08-16
## 1st Qu.: 15.0 1st Qu.:2016-02-28
## Median : 27.0 Median :2016-05-06
## Mean : 102.6 Mean :2016-05-11
## 3rd Qu.: 75.0 3rd Qu.:2016-08-18
## Max. : 18000.0 Max. :2016-11-27
##
## receipt_desc memo_cd
## :162356 :139603
## Refund : 883 X: 24603
## REDESIGNATION FROM PRIMARY : 232
## REDESIGNATION TO GENERAL : 225
## REDESIGNATION TO CRUZ FOR SENATE: 144
## SEE REDESIGNATION : 116
## (Other) : 250
## memo_text form_tp
## :98139 SA17A:139760
## * EARMARKED CONTRIBUTION: SEE BELOW:52328 SA18 : 23563
## * HILLARY VICTORY FUND :12312 SB28A: 883
## REDESIGNATION FROM PRIMARY : 232
## REDESIGNATION TO GENERAL : 225
## EARMARKED FROM MAKE DC LISTEN : 163
## (Other) : 807
## file_num tran_id election_tp zip
## Min. :1003942 C10182166: 2 : 400 Length:164206
## 1st Qu.:1077648 C10233294: 2 G2016: 52017 Class :character
## Median :1093618 C10233584: 2 P2016:111788 Mode :character
## Mean :1095013 C10248517: 2 P2020: 1
## 3rd Qu.:1119042 C10258303: 2
## Max. :1134173 C10262748: 2
## (Other) :164194
## ZCTA_USE cand_party retiree_status
## Length:164206 democrat :124675 Length:164206
## Class :character republican : 38751 Class :character
## Mode :character libertarian: 502 Mode :character
## green : 219
## independent: 59
##
## #some of these people were contributing early and often. I'll compare those to the 2015-2016 contribution limits here later: http://www.fec.gov/info/contriblimitschart1516.pdf
summary(pfc$contb_receipt_amt)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -16300.0 15.0 27.0 102.6 75.0 18000.0#the histogram on contributions had a large group around zero -- are there a large number of zero contributions?
length(which(pfc$contb_receipt_amt == 0))## [1] 5summary(pfc$contb_receipt_dt)## Min. 1st Qu. Median Mean 3rd Qu.
## "2014-08-16" "2016-02-28" "2016-05-06" "2016-05-11" "2016-08-18"
## Max.
## "2016-11-27"pfc %>%
group_by(memo_text) %>%
dplyr::summarise(n = n()) %>%
arrange(desc(n))## # A tibble: 95 × 2
## memo_text n
## <fctr> <int>
## 1 98139
## 2 * EARMARKED CONTRIBUTION: SEE BELOW 52328
## 3 * HILLARY VICTORY FUND 12312
## 4 REDESIGNATION FROM PRIMARY 232
## 5 REDESIGNATION TO GENERAL 225
## 6 EARMARKED FROM MAKE DC LISTEN 163
## 7 REDESIGNATION TO CRUZ FOR SENATE 144
## 8 *BEST EFFORTS UPDATE 135
## 9 SEE REDESIGNATION 116
## 10 REATTRIBUTION / REDESIGNATION REQUESTED 69
## # ... with 85 more rowsWhat is the structure of your dataset?
normalized, there’s one entry per donation (or refund)
What is/are the main feature(s) of interest in your dataset?
We could roll up to see how much each candidate received as a whole, or how much specific earmarks received, and drill down to see if someone in the state went over a contribution limit.
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
Each candidate is ID’d, and eafch contributor is specifically named, along with the date of hteir contribution and the amount of their contribution. Any memos with specific earmarks or refund memos are designated clearly.
Did you create any new variables from existing variables in the dataset?
No.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
I transformed the date into date format to make summary analysis simpler on that variable. There were a lot of donations that seemed centered around zero. I did exclude the top 1% while looking at these so I could see the data a bit closer, there are only 5 actual zero contributions. The others in the near-zero range appear to be micro-transactions and not actually zero.
Bivariate Plots Section
## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1395 rows containing non-finite values (stat_bin).## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 414 rows containing missing values (geom_bar).
#roll that up by party, keep log scale as total coming into various parties has lg difference
p <- ggplot(aes(x = contb_receipt_amt), data = subset(pfc, !is.na(contb_receipt_amt) & !is.na(cand_party))) +
geom_histogram() +
scale_x_log10() +
scale_y_log10() +
facet_wrap( ~ cand_party)
#run through plotly to clean up vis a bit & add mouseovers
ggplotly(p)## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1395 rows containing non-finite values (stat_bin).## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 3032 rows containing non-finite values (stat_bin).## Warning: Removed 15 rows containing missing values (geom_path).
## Warning: Removed 78 rows containing missing values (geom_point).
pfc %>%
#mutate to add a column for "EARMARK" in memo vs not
mutate(earmarked = if_else(
grepl("EARMARK", memo_text), "earmarked", "not_earmarked"
), cand_party) %>%
group_by(earmarked, cand_party) %>%
dplyr::summarise(n = n()) %>%
#always ungroup when >1 group
ungroup() %>%
ggplot(aes(x = earmarked, y =n )) +
geom_bar(stat="identity") +
#add a y-scale since this is financial and the non-major parties received significantly less $
scale_y_log10() +
facet_wrap(~cand_party)
#I've heard there's a strong rural/urban funding divide -- visible in this data?
pfc.contb_by_zip <- pfc %>%
#only include contributions where the zip code is in CO, and the zip exists
semi_join(colo_zip_cw, by="ZCTA_USE") %>%
group_by(ZCTA_USE) %>%
dplyr::summarise(value = sum(contb_receipt_amt), n = n())
#choropleth requires region be named region
colnames(pfc.contb_by_zip)[1] <- "region"
#use the ZCTA_USE list (Colorado zips in ZCTA format) to zoom:
zip_choropleth(pfc.contb_by_zip, title = "Donations by Zip Code", zip_zoom = colo_zip_cw$ZCTA_USE)## Warning in self$bind(): The following regions were missing and are being
## set to NA: 80727, 81330, 81126, 81044, 81642, 81081, 80862, 80476, 80546,
## 81640, 81155, 81030, 81124, 80745, 81633, 80801, 81021, 81029, 80471,
## 81077, 81092, 80757, 81225, 80740, 81045, 81638, 81087, 80747, 80726,
## 81649, 80622, 80310, 80913, 80479, 80744, 81084, 81227, 80024, 81129,
## 81231, 81239, 81248, 80754, 80419, 80938, 81027, 80939, 80511, 80746,
## 81334, 81038, 81148, 81043, 80294, 80423
rm(pfc.contb_by_zip)
##this seems to reflect the histogram of cities showing that most of the $ came from urbanBivariate Analysis
## # A tibble: 35 × 3
## memo_text n
## <fctr> <int>
## 1 * EARMARKED CONTRIBUTION: SEE BELOW REATTRIBUTION/REFUND PENDING 23
## 2 REFUNDED ON 10/24/2016 9
## 3 REFUND TO BE ISSUED 6
## 4 * EARMARKED CONTRIBUTION: SEE BELOW REFUNDED ON 10/24/2016 3
## 5 REFUNDED ON 10/10/2016 3
## 6 REATTRIBUTION/REFUND PENDING 2
## 7 REFUNDED $1000.00 ON 12/29/2015 2
## 8 REFUNDED ON 10/18/2016 2
## 9 REFUNDED ON 7/12/2016 2
## 10 $0.83 REFUNDED ON 10/25/2016 1
## # ... with 25 more rows, and 1 more variables: meanrefund <dbl>## # A tibble: 25 × 6
## cand_nm total_donation mean_donation median_donation
## <fctr> <dbl> <dbl> <dbl>
## 1 Clinton, Hillary Rodham 8544967.4 120.61837 25
## 2 Sanders, Bernard 2298527.4 42.81189 27
## 3 Trump, Donald J. 2456085.8 179.04110 60
## 4 Cruz, Rafael Edward 'Ted' 1011921.4 78.12255 50
## 5 Carson, Benjamin S. 694383.1 97.63541 50
## 6 Rubio, Marco 632342.2 287.29768 100
## 7 Fiorina, Carly 223509.6 236.26813 100
## 8 Paul, Rand 105243.5 160.67716 50
## 9 Johnson, Gary 128571.1 256.11771 100
## 10 Bush, Jeb 276847.0 692.11750 250
## # ... with 15 more rows, and 2 more variables: stdev_donation <dbl>,
## # n <int>## # A tibble: 5 × 5
## cand_party total_donation mean_donation median_donation n
## <fctr> <dbl> <dbl> <dbl> <int>
## 1 democrat 10930322.95 87.67053 25 124675
## 2 republican 5762403.22 148.70334 50 38751
## 3 libertarian 128571.09 256.11771 100 502
## 4 green 21388.27 97.66333 50 219
## 5 independent 12156.00 206.03390 100 59## # A tibble: 616 × 5
## contbr_city total_donation mean_donation median_donation n
## <fctr> <dbl> <dbl> <dbl> <int>
## 1 DENVER 4190800.7 130.08849 27 32215
## 2 BOULDER 1752512.3 117.33478 27 14936
## 3 COLORADO SPRINGS 1018770.6 92.28830 30 11039
## 4 FORT COLLINS 520008.9 69.39003 25 7494
## 5 ENGLEWOOD 461519.4 192.54043 35 2397
## 6 LITTLETON 454958.3 83.31044 27 5461
## 7 ASPEN 432404.5 428.54759 40 1009
## 8 AURORA 376474.7 61.62624 25 6109
## 9 GREENWOOD VILLAGE 333703.3 324.93023 75 1027
## 10 CENTENNIAL 330993.5 100.94341 28 3279
## # ... with 606 more rows#news stories frequently reported an age difference between party donation
donations_by_retiree <-pfc %>%
group_by(retiree_status, cand_party) %>%
dplyr::summarise(total_donation = sum(contb_receipt_amt),
mean_donation = mean(contb_receipt_amt),
median_donation = median(contb_receipt_amt),
n = n()) %>%
ungroup()
donations_by_retiree## # A tibble: 10 × 6
## retiree_status cand_party total_donation mean_donation median_donation
## <chr> <fctr> <dbl> <dbl> <dbl>
## 1 not_retired democrat 8659734.69 88.92450 27.0
## 2 not_retired republican 3794510.73 171.51106 50.0
## 3 not_retired libertarian 117956.59 262.12576 100.0
## 4 not_retired green 15388.27 93.26224 50.0
## 5 not_retired independent 8288.50 184.18889 100.0
## 6 retired democrat 2270588.26 83.19611 25.0
## 7 retired republican 1967892.49 118.35523 50.0
## 8 retired libertarian 10614.50 204.12500 100.0
## 9 retired green 6000.00 111.11111 50.0
## 10 retired independent 3867.50 276.25000 37.5
## # ... with 1 more variables: n <int>## [1] 0.2#were earmarked funds more likely to be higher amounts?
pfc %>%
group_by(if_else(
grepl("EARMARK", memo_text), "Earkmarked", "Not Earmarked"
)) %>%
dplyr::summarise(total_donation = sum(contb_receipt_amt),
mean_donation = mean(contb_receipt_amt),
median_donation = median(contb_receipt_amt),
n = n()) ## # A tibble: 2 × 5
## `if_else(grepl("EARMARK", memo_text), ...` total_donation mean_donation
## <chr> <dbl> <dbl>
## 1 Earkmarked 2262311 43.06129
## 2 Not Earmarked 14592530 130.67665
## # ... with 2 more variables: median_donation <dbl>, n <int>#were Republicans/Democrats more likely to earmark funds?
pfc %>%
#mutate to add a column for "EARMARK" in memo vs not
mutate(earmarked = if_else(
grepl("EARMARK", memo_text), "earmarked", "not_earmarked"
), cand_party) %>%
group_by(earmarked, cand_party) %>%
dplyr::summarise(n = n()) %>%
#always ungroup when >1 group
ungroup() %>%
#only show democrats & republicans so this will be a 2x2
filter(cand_party %in% c("democrat", "republican")) %>%
#spread into 2x2
tidyr::spread(earmarked, n) %>%
#remove explicit democrat/republican name column
select(earmarked, not_earmarked) %>%
#translate into data matrix for phi friendliness
data.matrix() %>%
#return two digits
phi(digits = 2)## [1] 0.38#phi is 0.38, a bit further from zero, weak positive associationTalk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
I found it really interesting that the Democratic party did fundraise successfully using smaller, more frequent, donations than the Republican party. I had read articles and heard news stories alleging this was their tactic, and it’s interesting to see how that strategy was successful in Colorado.
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
I was previously unaware of how many refunds existed in election fundraising.
It was interesting, but not incredibly surprising, to see the total funding by region mirror population density.
What was the strongest relationship you found?
A weak positive association between earmarks and democrats. Democrats were more likely to use earmarks. Combining this with the summarised earmark data, these were also for lower amounts than the average donation.
Multivariate Plots Section
## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 116 rows containing missing values (geom_point).
#Were different parties getting more for retirees vs non-retirees?
ggplot(aes(x = cand_party, y=total_donation), data = filter(donations_by_retiree, cand_party %in% c("democrat", "republican"))) +
geom_col(aes(fill = retiree_status))
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 81241, 80727, 80825, 81025, 80861, 81330, 81126, 81044, 81642,
## 80131, 81653, 81081, 80818, 80427, 80434, 80862, 80476, 80729, 80830,
## 81024, 80546, 81325, 81640, 81155, 81030, 80448, 81124, 81136, 80103,
## 80611, 80650, 80728, 80745, 80749, 80750, 80742, 80137, 80743, 81335,
## 81525, 81633, 80802, 80805, 81320, 80807, 80801, 80815, 80832, 80834,
## 80864, 80459, 80451, 80928, 81021, 81029, 80471, 80483, 81058, 81062,
## 81064, 81071, 81073, 81077, 81092, 81331, 80426, 80741, 80812, 80735,
## 80757, 80804, 81225, 80649, 80740, 81045, 80475, 81638, 80755, 80836,
## 81087, 80653, 80747, 80726, 80823, 81649, 80622, 81146, 80310, 80824,
## 80913, 81041, 81057, 80479, 80833, 80744, 81084, 81253, 81227, 80024,
## 81624, 81129, 81132, 81151, 81231, 81239, 80705, 81248, 80737, 81422,
## 80754, 81434, 80810, 81646, 81648, 81655, 80419, 80914, 80938, 80468,
## 80473, 81027, 81049, 81069, 81630, 81059, 80939, 80511, 80293, 80746,
## 81140, 81610, 81334, 81038, 81148, 80624, 81431, 81043, 80294, 80423, 80623
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 80727, 80652, 81330, 81126, 80733, 81044, 81642, 81081, 80862,
## 80476, 80546, 81640, 81155, 81030, 81124, 81128, 80025, 80101, 80722,
## 81426, 80745, 81633, 80801, 80822, 80420, 80455, 81021, 81029, 80469,
## 80471, 81077, 81092, 80497, 80478, 80757, 81225, 80740, 81045, 80457,
## 81138, 81638, 81087, 80747, 80726, 81649, 80622, 80310, 81332, 80913,
## 80479, 80744, 81084, 81227, 80024, 81129, 81152, 81231, 81239, 81248,
## 80721, 81411, 81433, 80754, 80419, 80938, 81027, 81076, 80939, 80511,
## 80746, 81334, 81038, 81148, 81043, 80294, 80423, 81040, 80264, 81243, 81033
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 81241, 80727, 80825, 81025, 80861, 80652, 81330, 81126, 80733,
## 81044, 81642, 80131, 81653, 81081, 80818, 80427, 80434, 80862, 80476,
## 80729, 80830, 81024, 80546, 81325, 81640, 81155, 81030, 80448, 81124,
## 81128, 80025, 81136, 80101, 80103, 80611, 80650, 80722, 81426, 80728,
## 80745, 80749, 80750, 80742, 80137, 80743, 81335, 81525, 81633, 80802,
## 80805, 81320, 80807, 80801, 80815, 80832, 80834, 80864, 80822, 80420,
## 80459, 80451, 80455, 80928, 81021, 81029, 80469, 80471, 80483, 81058,
## 81062, 81064, 81071, 81073, 81077, 81092, 80497, 81331, 80426, 80741,
## 80478, 80812, 80735, 80757, 80804, 81225, 80649, 80740, 81045, 80475,
## 80457, 81138, 81638, 80755, 80836, 81087, 80653, 80747, 80726, 80823,
## 81649, 80622, 81146, 80310, 81332, 80824, 80913, 81041, 81057, 80479,
## 80833, 80744, 81084, 81253, 81227, 80024, 81624, 81129, 81132, 81151,
## 81152, 81231, 81239, 80705, 81248, 80721, 80737, 81411, 81422, 81433,
## 80754, 81434, 80810, 81646, 81648, 81655, 80419, 80914, 80938, 80468,
## 80473, 81027, 81049, 81069, 81076, 81630, 81059, 80939, 80511, 80293,
## 80746, 81140, 81610, 81334, 81038, 81148, 80624, 81431, 81043, 80294,
## 80423, 81040, 80623, 80264, 81243, 81033
Multivariate Analysis
## [1] 0.15## [1] -0.07##
## Calls:
## m1: lm(formula = I(total_donation) ~ I(number_of_donations), data = frequent_contributors)
## m2: lm(formula = I(total_donation) ~ I(number_of_donations) + cand_party,
## data = frequent_contributors)
##
## ==================================================================
## m1 m2
## ------------------------------------------------------------------
## (Intercept) 393.599*** 492.426***
## (5.170) (7.283)
## I(number_of_donations) 17.367*** 14.862***
## (0.513) (0.527)
## cand_party: republican/democrat -183.081***
## (9.498)
## cand_party: libertarian/democrat -33.675
## (53.518)
## cand_party: green/democrat -273.951**
## (94.900)
## cand_party: independent/democrat -116.455
## (157.603)
## ------------------------------------------------------------------
## R-squared 0.0 0.0
## adj. R-squared 0.0 0.0
## sigma 866.9 862.4
## F 1145.8 306.9
## p 0.0 0.0
## Log-likelihood -291157.0 -290969.7
## Deviance 26736283722.0 26456217911.4
## AIC 582320.0 581953.4
## BIC 582345.5 582012.7
## N 35577 35577
## ==================================================================^That is an awful model.
I’m not seeing a predictible relationship between the variables here, I suspect there’s hidden motivators not in this data set.
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
Donations mostly came from urban areas. Urban areas were also more likely to be donating Democratic. Combined, the urban areas were donating significantly more to the Democratic party.
Were there any interesting or surprising interactions between features?
It was also interesting that retirees were donating higher amounts to the Republican party. As a whole, retirees donated almost purple–the sum of democrat donations vs the sum of republican donations is very similar as a part of the total donations in Colorado.
OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
I created a model and was completely unable to predict where someone would donate based on
Final Plots and Summary
Plot One
Description One
The majority of moneys donated in the 2016 election from Colorado residents was for Democratic candidates; 10.93 million Democratic to 5.76 million Republican.
Plot Two
Description Two
The Democrat/Republican donation split for retirees in Colorado is almost purple, barely leaning Democratic. However, the non-retiree population’s donations are financially heavily Democratic. This fits in nicely with the local wisdom that the urban areas vote Democratic, while the prarie votes Republican.
Plot Three
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 81241, 80727, 80825, 81025, 80861, 80652, 81330, 81126, 80733,
## 81044, 81642, 80131, 81653, 81081, 80818, 80427, 80434, 80862, 80476,
## 80729, 80830, 81024, 80546, 81325, 81640, 81155, 81030, 80448, 81124,
## 81128, 80025, 81136, 80101, 80103, 80611, 80650, 80722, 81426, 80728,
## 80745, 80749, 80750, 80742, 80137, 80743, 81335, 81525, 81633, 80802,
## 80805, 81320, 80807, 80801, 80815, 80832, 80834, 80864, 80822, 80420,
## 80459, 80451, 80455, 80928, 81021, 81029, 80469, 80471, 80483, 81058,
## 81062, 81064, 81071, 81073, 81077, 81092, 80497, 81331, 80426, 80741,
## 80478, 80812, 80735, 80757, 80804, 81225, 80649, 80740, 81045, 80475,
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## 80423, 81040, 80623, 80264, 81243, 81033## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=39.028453,-106.048697&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 1 rows containing missing values (geom_rect).
Description Three
I overlaid the map to show how high population areas and ‘resort’ areas donated significantly more funds to Democratic candidates than Republican candidates in the 2016 election cycle. The N/A areas are areas with very low population. I’m using ZCTA instead of true Zip code because choropleth runs off ZCTA, as does census data. In this graph, the bluest areas are the areas with the highest funding difference in favor of the Democratic party.
Reflection
I validated several stories that had been trending in the news during the election cycle in this data set.
I often heard Colorado referred to as ‘purple’, leaning between Democratic and Republican, so I checked to see if the financial donations reflected that even split. This was not the case in the overall donation sums between the two parties.
In detail, I verified the news story that the Democratic funding strategy successfully raised more money by raising more frequent, smaller, donations while the Republican party went for larger, less frequent donations. On average, the Colorado donations were 50 dollars for Democrats and 100 dollars for republicans. In the violin chart, there’s a large visual difference between the large amount donations in the two parties.
I suspected the difference between the financials could be due to the differnce between older and younger contributors, as the millenial vote was strongly Democratic this election season. That was validated in the data–the split between retirees was almost even, while the split between non-retirees was rather high–8659734.69 dollars to 3794510.73 dollars.
Lastly, I’ve heard that the red/blue split is heavily influenced by geography, with Republican voters living in the prarie and Democratic voters living in the mountains and highly populated urban areas. This was validated in the data as seen in the difference between blue/Democratic fundraising and white/Republican fundraising in the choropleth map.